Language Models for Code demonstrate significant potential in software engineering, and augmenting language models with various software components has garnered attention. However, the field lacks a comprehensive survey that systematically examines these approaches across different dimensions of code intelligence enhancement. This survey fills this critical gap by providing a comprehensive exploration of augmentation strategies across diverse software engineering tasks. We systematically categorize and analyze a broad spectrum of techniques, encompassing: (1) retrieval-augmented approaches that leverage external code repositories and documentation to enhance contextual understanding, (2) structural knowledge integration methods that incorporate code syntax and semantic patterns into model reasoning, and (3) external tool integration strategies that enable interaction with Application Programming Interfaces (APIs) and development environments. Furthermore, we examine the emerging agent paradigms that transform static code language models into autonomous systems capable of complex software engineering workflows. To our knowledge, this survey establishes the first comprehensive foundation for providing the current landscape of different augmentation techniques for code, providing key research directions for advancing code intelligence.
Lee et al. (Thu,) studied this question.
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